Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

The regularization principals [31] lead approximation schemes to deal with various learning
problems, eg, the regularization of the norm in a reproducing kernel Hilbert space for the ill …

Abstract Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the
class relationship between data points. A major disadvantage of LDA is that it fails to …

Social networks, such as Twitter, form a heterogeneous information network (HIN) where
nodes represent domain entities (eg, user, content, advertiser, etc.) and edges represent …

This paper develops an unsupervised discriminant projection (UDP) technique for
dimensionality reduction of high-dimensional data in small sample size cases. UDP can be …

A large family of algorithms for dimensionality reduction end with solving a Trace Ratio
problem in the form of arg max W Tr (WTSPW)/Tr (WT SIW) 1, which is generally transformed …

Subspace learning based face recognition methods have attracted considerable interests in
recent years, including principal component analysis (PCA), linear discriminant analysis …

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have
been proposed to improve the recommendation accuracy in a target dataset …